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Exploring Apache Superset as a Visualization Tool for Oracle R12

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Exploring Apache Superset as a Visualization Tool for Oracle R12

Apache SuperSet is an Open Source data visualization tool that can be used for graphical representation of data. A tutorial on how to use it.

· Open Source Zone ·
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Apache SuperSet is an Open Source data visualization tool that can be used for graphical representation of data. Superset was initially created by AirBnB and later released to the Apache community. Apache Superset is developed in Python language and utilizes Flask Framework for all web interactions. Superset supports the majority of RDMBS via SQL Alchemy. 

POC Environment:

  • OS - Ubuntu 18 Linux 64 Bit.
  • Python – 3.6.9 
  • Apache Superset v0.36.0.
  • Data source - Oracle ERP R12
  • Database version - Oracle 11g 

The POC intends to build a dashboard to show the logistics demand of an organization. Various charts can be used to slice the data and create a dashboard to interpret the data

Step 1: Create Data Source

 Configure development instance credentials in the data source page. As a prerequisite make sure python library CX_Oracle along with Oracle client are installed.

 

https://dzone.com/storage/temp/13795416-1596026819092.png

 

Add a new database and enter details as in the screenshot. Make sure “Expose in SQL Lab” and “Allow Multi Schema Metadata Fetch” is checked.

https://dzone.com/storage/temp/13795757-1596041820767.png

Step 2: Create Data Set

 Here we will be creating MVIEW as a data source. One of the key issues is data accuracy. Make sure there is some data cleansing performed on the data set. In this scenario, the MVIEW has relevant data of all orders for the past few years with appropriate filters to qualify the data. 

Step 3: Create Charts

Superset has many charts to choose from. The chart of interest for this POC is the Arc map. This map can be used to display a global snapshot of logistics demand. This is accomplished by loading latitude and longitude with postal codes and country combinations. For the map to work, we will need to set MAPBOX_API_KEY token in superset_config.py configuration file. The token can be requested at https://www.mapbox.com/

Add the chart to a dashboard with filter options. 

https://dzone.com/storage/temp/13795424-1596027030263.png

 

Choosing appropriate filters, in this case, date range and distribution center, we can drill down to the required level of data granularity. 

https://dzone.com/storage/temp/13795426-1596027151112.png

 

https://dzone.com/storage/temp/13795747-1596041388945.png

 

Conclusion

Apache Superset with its caching capability, ability to connect to most of RDBMS, and inbuilt security features makes it quite a versatile tool. Around 40 visualizations are available for slicing and dicing the data. With these features, Superset can definitely be considered as an alternate option available in the open-source market. 

 PS: Would like to try out Apache Superset with Apache Ignite. Let me know if I should!

Topics:
apache superset

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